keract | Layers Outputs and Gradients in Keras. Made easy. | Machine Learning library

by philipperemy Python Version: 4.5.0 License: MIT

X-Ray Key Features Code Snippets Community Discussions(1)Vulnerabilities Install Support

kandi X-RAY | keract Summary

keract is a Python library typically used in Artificial Intelligence, Machine Learning, Deep Learning, Tensorflow, Keras, Neural Network applications. keract has no bugs, it has no vulnerabilities, it has build file available, it has a Permissive License and it has medium support. You can download it from GitHub.

Layers Outputs and Gradients in Keras. Made easy.

Support

Quality

Security

License

Reuse

Support

keract has a medium active ecosystem.

It has 1021 star(s) with 187 fork(s). There are 34 watchers for this library.

It had no major release in the last 12 months.

There are 2 open issues and 86 have been closed. On average issues are closed in 57 days. There are no pull requests.

It has a neutral sentiment in the developer community.

The latest version of keract is 4.5.0

Quality

keract has 0 bugs and 0 code smells.

Security

keract has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.

keract code analysis shows 0 unresolved vulnerabilities.

There are 0 security hotspots that need review.

License

keract is licensed under the MIT License. This license is Permissive.

Permissive licenses have the least restrictions, and you can use them in most projects.

Reuse

keract releases are available to install and integrate.

Build file is available. You can build the component from source.

Installation instructions are not available. Examples and code snippets are available.

keract saves you 448 person hours of effort in developing the same functionality from scratch.

It has 1061 lines of code, 64 functions and 18 files.

It has high code complexity. Code complexity directly impacts maintainability of the code.

Top functions reviewed by kandi - BETA

kandi has reviewed keract and discovered the below as its top functions. This is intended to give you an instant insight into keract implemented functionality, and help decide if they suit your requirements.

Display heatmaps
Get activations from the given model
Evaluate the model
Get the nodes from the module
Return node name
Displays activations
Convert 1D to 2d array
Get gradients of activations
Compute the gradients for each node
Create a network with a single subnet
Load MNIST dataset
Get gradients of trainable weights
Enable GPU memory growth
Load activations from a json file
Get a multi - output model
Persist activations to a JSON file
Print out the name and shape of each layer
Returns a model for multi - inputs
Print the names and values of activations
Create a single input model

Get all kandi verified functions for this library.

keract Key Features

No Key Features are available at this moment for keract.

keract Examples and Code Snippets

No Code Snippets are available at this moment for keract.

Community Discussions

Trending Discussions on keract

CNN Visualization of output layers with pre-trained model

QUESTION

CNN Visualization of output layers with pre-trained model

Asked 2020-Aug-05 at 07:20

I trained my model and saved it:

...

ANSWER

Answered 2020-Aug-05 at 07:20

Okey, i found a convenient solution to my problem.

Indeed i think this problem occurs because my sequential model is itself made up of another model (resnet).

Since i didn't add many layers on top of the pre-trained resnet model, i just decided to visualize the feature maps from the resnet model

Source https://stackoverflow.com/questions/63245102

Community Discussions, Code Snippets contain sources that include Stack Exchange Network

Vulnerabilities

No vulnerabilities reported

Install keract

You can download it from GitHub.
You can use keract like any standard Python library. You will need to make sure that you have a development environment consisting of a Python distribution including header files, a compiler, pip, and git installed. Make sure that your pip, setuptools, and wheel are up to date. When using pip it is generally recommended to install packages in a virtual environment to avoid changes to the system.

Support

For any new features, suggestions and bugs create an issue on GitHub. If you have any questions check and ask questions on community page Stack Overflow .

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